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Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco / Hassane Rahali in Geocarto international, vol 34 n° 1 ([01/01/2019])  

Public [article]  inGeocarto international > vol 34 n° 1 [01/01/2019] . - pp 43 - 77 Titre : Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco Type de document : Article/Communication Auteurs : Hassane Rahali, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse des risques [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] lithologie [Termes descripteurs IGN] Maroc [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] méthode fiable [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] processus stochastique [Termes descripteurs IGN] régression logistique [Termes descripteurs IGN] théorème de Bayes [Termes descripteurs IGN] zone à risque Résumé : (auteur) This paper aims at providing an answer as to whether generalization obtained with data-driven modelling can be used to gauge the plausibility of the physically based (PB) model’s prediction. Two statistical models namely; Weight of Evidence (WofE) and Logistic Regression (LR), and a PB model using the infinite slope assumptions were evaluated and compared with respect to their abilities to predict susceptible areas to shallow landslides at the 1:10.000 urban scale. Threshold-dependent performance metrics showed that the three methods produced statistically comparable results in terms of success and prediction rates. However, with the Area Under the receiver operator Curve (AUC), statistical models are more accurate (88.7 and 84.6% for LR and WofE, respectively) than the PB model (only 69.8%). Nevertheless, in such data-sparse situation, the usual approaches for validation, i.e. comparing observed with predicted data, are insufficient, formal uncertainty analysis (UA) is a means for evaluating the validity and reliability of the model. We then refitted the PB model using a stochastic modification of the infinite slope stability model input scheme using Monte Carlo (MC) method backed with sensitivity analysis (SA). For statistical models, we used an informal Student t-test for estimating the certainty of the predicted probability (PP) at each location. Both modelling outputs independently show a high validity; and whereas the level of confidence in LR and WofE models remained the same after performance re-evaluation, the accuracy of the PB model showed an improvement (AUC = 72%). This result is reasonable and provides a further validation of PB model. So, in urban slope analysis, where PB diagnostic is necessary, statistical and PB modelling may play equally supportive roles in landslide hazard assessment. Numéro de notice : A2019-219 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1357767 date de publication en ligne : 10/08/2017 En ligne : https://doi.org/10.1080/10106049.2017.1357767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92737 [article]

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